Benchmarking Badminton Action Recognition with a New Fine-Grained Dataset

TL;DR

This paper introduces the VideoBadminton dataset, a fine-grained video dataset for badminton action recognition, and evaluates leading methods to advance sports-specific action understanding in computer vision.

Contribution

The paper presents a new detailed badminton action dataset and benchmarks existing methods, addressing the need for fine-grained sports action recognition datasets.

Findings

Leading methods show varied performance on the new dataset.

Fine-grained distinctions in badminton actions pose challenges for current models.

The dataset enables targeted research in sports action recognition.

Abstract

In the dynamic and evolving field of computer vision, action recognition has become a key focus, especially with the advent of sophisticated methodologies like Convolutional Neural Networks (CNNs), Convolutional 3D, Transformer, and spatial-temporal feature fusion. These technologies have shown promising results on well-established benchmarks but face unique challenges in real-world applications, particularly in sports analysis, where the precise decomposition of activities and the distinction of subtly different actions are crucial. Existing datasets like UCF101, HMDB51, and Kinetics have offered a diverse range of video data for various scenarios. However, there's an increasing need for fine-grained video datasets that capture detailed categorizations and nuances within broader action categories. In this paper, we introduce the VideoBadminton dataset derived from high-quality badminton footage. Through an exhaustive evaluation of leading methodologies on this dataset, this study aims to advance the field of action recognition, particularly in badminton sports. The introduction of VideoBadminton could not only serve for badminton action recognition but also provide a dataset for recognizing fine-grained actions. The insights gained from these evaluations are expected to catalyze further research in action comprehension, especially within sports contexts.